Control Barriers in Bayesian Learning of System Dynamics

This article focuses on learning a model of system dynamics online, while satisfying safety constraints. Our objective is to avoid offline system identification or hand-specified models and allow a system to safely and autonomously estimate and adapt its own model during operation. Given streaming o...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on automatic control Vol. 68; no. 1; pp. 214 - 229
Main Authors:	Dhiman, Vikas, Khojasteh, Mohammad Javad, Franceschetti, Massimo, Atanasov, Nikolay
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Bayes methods Bayesian analysis Control barrier function (CBF) Control systems Gaussian process Gaussian processes high relative-degree system safety learning for dynamics and control Liapunov functions Machine learning Nonlinear control Nonlinear dynamical systems Probabilistic logic Safety self-triggered safe control Stability analysis Statistical analysis System dynamics System identification
ISSN:	0018-9286, 1558-2523
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	This article focuses on learning a model of system dynamics online, while satisfying safety constraints. Our objective is to avoid offline system identification or hand-specified models and allow a system to safely and autonomously estimate and adapt its own model during operation. Given streaming observations of the system state, we use Bayesian learning to obtain a distribution over the system dynamics. Specifically, we propose a new matrix variate Gaussian process (MVGP) regression approach with an efficient covariance factorization to learn the drift and input gain terms of a nonlinear control-affine system. The MVGP distribution is then used to optimize the system behavior and ensure safety with high probability, by specifying control Lyapunov function (CLF) and control barrier function (CBF) chance constraints. We show that a safe control policy can be synthesized for systems with arbitrary relative degree and probabilistic CLF-CBF constraints by solving a second-order cone program. Finally, we extend our design to a self-triggering formulation, adaptively determining the time at which a new control input needs to be applied in order to guarantee safety.
AbstractList	This article focuses on learning a model of system dynamics online, while satisfying safety constraints. Our objective is to avoid offline system identification or hand-specified models and allow a system to safely and autonomously estimate and adapt its own model during operation. Given streaming observations of the system state, we use Bayesian learning to obtain a distribution over the system dynamics. Specifically, we propose a new matrix variate Gaussian process (MVGP) regression approach with an efficient covariance factorization to learn the drift and input gain terms of a nonlinear control-affine system. The MVGP distribution is then used to optimize the system behavior and ensure safety with high probability, by specifying control Lyapunov function (CLF) and control barrier function (CBF) chance constraints. We show that a safe control policy can be synthesized for systems with arbitrary relative degree and probabilistic CLF-CBF constraints by solving a second-order cone program. Finally, we extend our design to a self-triggering formulation, adaptively determining the time at which a new control input needs to be applied in order to guarantee safety.
Author	Franceschetti, Massimo Khojasteh, Mohammad Javad Atanasov, Nikolay Dhiman, Vikas
Author_xml	– sequence: 1 givenname: Vikas orcidid: 0000-0003-0078-3677 surname: Dhiman fullname: Dhiman, Vikas email: vikas.dhiman@maine.edu organization: Department of Electrical and Computer Engineering, University of Maine, Bangor, ME, USA – sequence: 2 givenname: Mohammad Javad orcidid: 0000-0002-8459-6483 surname: Khojasteh fullname: Khojasteh, Mohammad Javad email: mkhojast@mit.edu organization: Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA – sequence: 3 givenname: Massimo orcidid: 0000-0002-4057-8152 surname: Franceschetti fullname: Franceschetti, Massimo email: massimo@ece.ucsd.edu organization: Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA – sequence: 4 givenname: Nikolay orcidid: 0000-0003-0272-7580 surname: Atanasov fullname: Atanasov, Nikolay email: natanasov@eng.ucsd.edu organization: Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
BookMark	eNp9kEtLAzEURoNUsK3uBTcDrqfmMXkt61gfUHBhXYdMuJGUNlOT6WL-vTO0uHDh6t4L37kfnBmaxDYCQrcELwjB-mGzrBcUU7JghEnM9QWaEs5VSTllEzTFmKhSUyWu0Czn7XCKqiJTpOo2dqndFY82pQApFyEOew852FiswaYY4lfR-uKjzx3si6c-2n1w-RpdervLcHOec_T5vNrUr-X6_eWtXq5LRzXpSuk98dxa7IVk3DJqnfNM60YIBaxhrHGV9NBgEJXWpKKVk1SClw477rhkc3R_-ntI7fcRcme27THFodJQyZWkVLExhU8pl9qcE3hzSGFvU28INqMfM_gxox9z9jMg4g_iQme7MPqwYfcfeHcCAwD89mjBFaGM_QAy8HMF
CODEN	IETAA9
CitedBy_id	crossref_primary_10_1109_TRO_2025_3577022 crossref_primary_10_1109_TASE_2023_3305485 crossref_primary_10_1016_j_automatica_2024_111800 crossref_primary_10_1109_LRA_2022_3182544 crossref_primary_10_1109_JAS_2023_123075 crossref_primary_10_1109_TAC_2025_3550850 crossref_primary_10_1016_j_arcontrol_2024_100947 crossref_primary_10_1109_TASE_2023_3339501 crossref_primary_10_1016_j_arcontrol_2024_100945 crossref_primary_10_1109_LCSYS_2023_3235958 crossref_primary_10_1109_TCYB_2025_3539464 crossref_primary_10_1080_01691864_2024_2401897 crossref_primary_10_1109_TASE_2023_3341470 crossref_primary_10_1109_TCYB_2024_3390947 crossref_primary_10_1109_LCSYS_2025_3581519 crossref_primary_10_1109_TSMC_2023_3240290 crossref_primary_10_1177_02783649251352000 crossref_primary_10_1080_10095020_2025_2519374 crossref_primary_10_1109_LRA_2022_3216996 crossref_primary_10_1109_TAC_2023_3250514 crossref_primary_10_1016_j_ins_2023_119684 crossref_primary_10_1016_j_automatica_2025_112263 crossref_primary_10_1002_rnc_6585 crossref_primary_10_1109_TSMC_2023_3292810
Cites_doi	10.1016/j.automatica.2021.109597 10.3182/20070822-3-za-2920.00076 10.1109/LCSYS.2020.3043287 10.5555/3045390.3045502 10.23919/ECC.2019.8795639 10.1109/TAC.2007.902736 10.1609/aaai.v33i01.33013387 10.23919/ACC.2019.8814901 10.23919/ACC45564.2020.9147463 10.1109/IROS45743.2020.9341190 10.1109/TAC.2016.2638961 10.23919/ACC.2019.8814657 10.1109/LRA.2021.3057023 10.1109/ICRA.2019.8793891 10.15607/rss.2018.xiv.056 10.1109/JPROC.2012.2189792 10.23919/ECC51009.2020.9143595 10.1109/CCTA.2017.8062713 10.1109/CDC42340.2020.9304160 10.1109/CDC.2018.8619572 10.15607/rss.2020.xvi.087 10.23919/ECC.2019.8796030 10.1109/CDC42340.2020.9303847 10.1109/ITSC45102.2020.9294485 10.1109/LCSYS.2019.2917975 10.1109/TCYB.2020.2980048 10.1016/S0024-3795(98)10032-0 10.1007/978-3-030-44051-0_19 10.1109/LRA.2020.2976272 10.1109/ICRA40945.2020.9196709 10.1109/ICRA.2018.8460471 10.1016/j.automatica.2018.07.004 10.1109/MCS.2003.1188769 10.1016/j.automatica.2020.109439 10.1007/978-1-4613-1193-5 10.1109/AERO47225.2020.9172271 10.1016/j.ifacol.2015.11.152 10.1609/aaai.v32i1.11797 10.1109/CDC42340.2020.9303776 10.1109/TCNS.2020.3028035 10.1109/ACC.2015.7172044 10.23919/ACC50511.2021.9483182 10.23919/ACC45564.2020.9147584 10.1109/CDC40024.2019.9029455 10.1109/TAC.2018.2876389 10.1561/2200000036 10.1007/s10208-019-09426-y 10.1109/CDC45484.2021.9683743 10.1017/CBO9781316576533 10.1109/CDC42340.2020.9303785 10.1109/ACC.2016.7524935 10.23919/ACC.2019.8815276 10.1109/CDC.2012.6425820 10.1109/ACC.2016.7526114 10.1109/TAC.2019.2958840 10.1109/TNNLS.2014.2319052 10.1109/CDC42340.2020.9303957 10.1016/j.automatica.2020.109009 10.1109/MRA.2010.936946 10.1109/CDC40024.2019.9029666 10.1109/ICRA.2016.7487170 10.1109/LCSYS.2020.3005923 10.23919/ACC.2018.8431275 10.1137/1.9780898718980
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
DBID	97E RIA RIE AAYXX CITATION 7SC 7SP 7TB 8FD FR3 JQ2 L7M L~C L~D
DOI	10.1109/TAC.2021.3137059
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional
DatabaseTitle	CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional
DatabaseTitleList	Technology Research Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE/IET Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1558-2523
EndPage	229
ExternalDocumentID	10_1109_TAC_2021_3137059 9658123
Genre	orig-research
GrantInformation_xml	– fundername: National Science Foundation grantid: CNS-1446891; ECCS-1917177; IIS-2007141 funderid: 10.13039/501100008982 – fundername: ARL DCIST CRA grantid: W911NF-17-2-0181
GroupedDBID	-~X .DC 0R~ 29I 3EH 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ H~9 IAAWW IBMZZ ICLAB IDIHD IFIPE IFJZH IPLJI JAVBF LAI M43 MS~ O9- OCL P2P RIA RIE RNS TAE TN5 VH1 VJK ~02 AAYXX CITATION 7SC 7SP 7TB 8FD FR3 JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c291t-7ff1f5aa0f6735a32accf399b668e3b33bc47feb0e64991424c727ef7c0c5c573
IEDL.DBID	RIE
ISICitedReferencesCount	37
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000921346300016&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0018-9286
IngestDate	Mon Jun 30 10:13:42 EDT 2025 Tue Nov 18 22:24:33 EST 2025 Sat Nov 29 05:41:04 EST 2025 Wed Aug 27 02:11:18 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	1
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c291t-7ff1f5aa0f6735a32accf399b668e3b33bc47feb0e64991424c727ef7c0c5c573
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-4057-8152 0000-0002-8459-6483 0000-0003-0078-3677 0000-0003-0272-7580
PQID	2758722837
PQPubID	85475
PageCount	16
ParticipantIDs	crossref_primary_10_1109_TAC_2021_3137059 ieee_primary_9658123 proquest_journals_2758722837 crossref_citationtrail_10_1109_TAC_2021_3137059
PublicationCentury	2000
PublicationDate	2023-Jan. 2023-1-00 20230101
PublicationDateYYYYMMDD	2023-01-01
PublicationDate_xml	– month: 01 year: 2023 text: 2023-Jan.
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on automatic control
PublicationTitleAbbrev	TAC
PublicationYear	2023
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref57 ref56 ref15 ref14 ref53 ref52 ref11 ref55 Khojasteh (ref1) 2020 ref10 ref54 ref17 ref19 Sarkar (ref77) 2020 ref18 Berkenkamp (ref63) 2017 Gardner (ref81) 2018 ref51 ref50 ref46 ref45 Taylor (ref80) 2009 Sarkar (ref8) 2019 ref48 ref47 ref42 ref41 ref44 ref43 Williams (ref59) 2006; 2 ref7 ref9 ref4 ref3 ref5 ref40 ref35 ref79 Deisenroth (ref6) 2011 ref34 ref78 ref37 (ref71) 2020 ref36 Liu (ref12) 2020 ref31 ref75 ref30 ref74 ref33 ref32 ref76 Levine (ref16) 2016; 17 ref2 ref39 ref38 Louizos (ref67) 2016 Searle (ref83) 1971 ref70 ref73 ref24 ref68 ref23 ref26 Sun (ref66) 2017 ref25 ref20 ref64 ref22 ref21 ref65 ref28 ref27 Sutton (ref82) 2018 ref29 Pereira (ref49) 2020 Taylor (ref58) 2020 ref60 Vandenberghe (ref72) 2010 ref62 ref61 Lederer (ref69) 2019
References_xml	– ident: ref43 doi: 10.1016/j.automatica.2021.109597 – ident: ref20 doi: 10.3182/20070822-3-za-2920.00076 – ident: ref50 doi: 10.1109/LCSYS.2020.3043287 – ident: ref60 doi: 10.5555/3045390.3045502 – ident: ref9 doi: 10.23919/ECC.2019.8795639 – start-page: 5610 volume-title: Proc. 36th Int. Conf. Mach. Learn. year: 2019 ident: ref8 article-title: Near optimal finite time identification of arbitrary linear dynamical systems – ident: ref24 doi: 10.1109/TAC.2007.902736 – ident: ref53 doi: 10.1609/aaai.v33i01.33013387 – ident: ref47 doi: 10.23919/ACC.2019.8814901 – ident: ref55 doi: 10.23919/ACC45564.2020.9147463 – ident: ref76 doi: 10.1109/IROS45743.2020.9341190 – ident: ref21 doi: 10.1109/TAC.2016.2638961 – ident: ref30 doi: 10.23919/ACC.2019.8814657 – volume: 17 start-page: 1334 issue: 1 year: 2016 ident: ref16 article-title: End-to-end training of deep visuomotor policies publication-title: J. Mach. Learn. Res. – ident: ref18 doi: 10.1109/LRA.2021.3057023 – ident: ref57 doi: 10.1109/ICRA.2019.8793891 – start-page: 1283 volume-title: Proc. 20th Int. Conf. Artif. Intell. Statist. year: 2017 ident: ref66 article-title: Learning structured weight uncertainty in Bayesian neural networks – ident: ref17 doi: 10.15607/rss.2018.xiv.056 – ident: ref2 doi: 10.1109/JPROC.2012.2189792 – ident: ref46 doi: 10.23919/ECC51009.2020.9143595 – ident: ref22 doi: 10.1109/CCTA.2017.8062713 – ident: ref26 doi: 10.1109/CDC42340.2020.9304160 – ident: ref39 doi: 10.1109/CDC.2018.8619572 – ident: ref13 doi: 10.15607/rss.2020.xvi.087 – ident: ref25 doi: 10.23919/ECC.2019.8796030 – ident: ref64 doi: 10.1109/CDC42340.2020.9303847 – ident: ref78 doi: 10.1109/ITSC45102.2020.9294485 – start-page: 7587 volume-title: Proc. 32nd Int. Conf. Neural Inf. Process. Syst. year: 2018 ident: ref81 article-title: GPyTorch: Blackbox matrix-matrix Gaussian process inference with GPU acceleration – ident: ref28 doi: 10.1109/LCSYS.2019.2917975 – ident: ref34 doi: 10.1109/TCYB.2020.2980048 – ident: ref70 doi: 10.1016/S0024-3795(98)10032-0 – start-page: 1708 volume-title: Proc. 33rd Int. Conf. Mach. Learn. year: 2016 ident: ref67 article-title: Structured and efficient variational deep learning with matrix Gaussian posteriors – start-page: 908 volume-title: Proc. 31st Int. Conf. Neural Inf. Process. Syst. year: 2017 ident: ref63 article-title: Safe model-based reinforcement learning with stability guarantees – volume-title: Linear Models year: 1971 ident: ref83 – ident: ref61 doi: 10.1007/978-3-030-44051-0_19 – start-page: 1783 volume-title: Proc. Conf. Robot Learn. year: 2020 ident: ref49 article-title: Safe optimal control using stochastic barrier functions and deep forward-backward SDEs – ident: ref5 doi: 10.1109/LRA.2020.2976272 – ident: ref51 doi: 10.1109/ICRA40945.2020.9196709 – ident: ref52 doi: 10.1109/ICRA.2018.8460471 – ident: ref27 doi: 10.1016/j.automatica.2018.07.004 – volume: 2 volume-title: Gaussian Processes for Machine Learning year: 2006 ident: ref59 – ident: ref3 doi: 10.1109/MCS.2003.1188769 – ident: ref48 doi: 10.1016/j.automatica.2020.109439 – ident: ref68 doi: 10.1007/978-1-4613-1193-5 – ident: ref19 doi: 10.1109/AERO47225.2020.9172271 – start-page: 465 volume-title: Proc. 28th Int. Conf. Mach. Learn. year: 2011 ident: ref6 article-title: PILCO: A model-based and data-efficient approach to policy search – ident: ref23 doi: 10.1016/j.ifacol.2015.11.152 – year: 2009 ident: ref80 article-title: Spatial statistics: notes on random fields – ident: ref45 doi: 10.1609/aaai.v32i1.11797 – ident: ref74 doi: 10.1109/CDC42340.2020.9303776 – start-page: 781 volume-title: Proc. 2nd Conf. Learn. Dyn. Control year: 2020 ident: ref1 article-title: Probabilistic safety constraints for learned high relative degree system dynamics – ident: ref11 doi: 10.1109/TCNS.2020.3028035 – ident: ref35 doi: 10.1109/ACC.2015.7172044 – year: 2010 ident: ref72 article-title: The CVXOPT linear and quadratic cone program solvers – start-page: 708 volume-title: Proc. 2nd Conf. Learn. Dyn. Control year: 2020 ident: ref58 article-title: Learning for safety-critical control with control barrier functions – start-page: 608 volume-title: Proc. 2nd Conf. Learn. Dyn. Control year: 2020 ident: ref12 article-title: Robust regression for safe exploration in control – ident: ref42 doi: 10.23919/ACC50511.2021.9483182 – ident: ref54 doi: 10.23919/ACC45564.2020.9147584 – ident: ref38 doi: 10.1109/CDC40024.2019.9029455 – year: 2020 ident: ref77 article-title: High-relative degree stochastic control Lyapunov and barrier functions – ident: ref41 doi: 10.1109/TAC.2018.2876389 – ident: ref65 doi: 10.1561/2200000036 – ident: ref7 doi: 10.1007/s10208-019-09426-y – ident: ref73 doi: 10.1109/CDC45484.2021.9683743 – ident: ref62 doi: 10.1017/CBO9781316576533 – ident: ref75 doi: 10.1109/CDC42340.2020.9303785 – ident: ref37 doi: 10.1109/ACC.2016.7524935 – volume-title: Gurobi Optimizer Reference Manual year: 2020 ident: ref71 – ident: ref44 doi: 10.23919/ACC.2019.8815276 – ident: ref33 doi: 10.1109/CDC.2012.6425820 – ident: ref36 doi: 10.1109/ACC.2016.7526114 – volume-title: Reinforcement Learning: An Introduction year: 2018 ident: ref82 – ident: ref31 doi: 10.1109/TAC.2019.2958840 – ident: ref14 doi: 10.1109/TNNLS.2014.2319052 – ident: ref15 doi: 10.1109/CDC42340.2020.9303957 – ident: ref32 doi: 10.1016/j.automatica.2020.109009 – ident: ref4 doi: 10.1109/MRA.2010.936946 – ident: ref29 doi: 10.1109/CDC40024.2019.9029666 – ident: ref40 doi: 10.1109/ICRA.2016.7487170 – start-page: 659 volume-title: Proc. 33rd Int. Conf. Neural Inf. Process. Syst. year: 2019 ident: ref69 article-title: Uniform error bounds for Gaussian process regression with application to safe control – ident: ref56 doi: 10.1109/LCSYS.2020.3005923 – ident: ref10 doi: 10.23919/ACC.2018.8431275 – ident: ref79 doi: 10.1137/1.9780898718980
SSID	ssj0016441
Score	2.587213
Snippet	This article focuses on learning a model of system dynamics online, while satisfying safety constraints. Our objective is to avoid offline system...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	214
SubjectTerms	Bayes methods Bayesian analysis Control barrier function (CBF) Control systems Gaussian process Gaussian processes high relative-degree system safety learning for dynamics and control Liapunov functions Machine learning Nonlinear control Nonlinear dynamical systems Probabilistic logic Safety self-triggered safe control Stability analysis Statistical analysis System dynamics System identification
Title	Control Barriers in Bayesian Learning of System Dynamics
URI	https://ieeexplore.ieee.org/document/9658123 https://www.proquest.com/docview/2758722837
Volume	68
WOSCitedRecordID	wos000921346300016&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE/IET Electronic Library (IEL) customDbUrl: eissn: 1558-2523 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016441 issn: 0018-9286 databaseCode: RIE dateStart: 19630101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEB7a4kEPvqpYrZKDF8G12013kz1qtXgqHir0tmSnEynIVrqt4L83j-1SUARvOSQQJsk8M98HcB1ZkLtEKnN5beomjONAociDGBNKIjWTgjzZhBiP5XSavjTgtu6FISL3-Yzu7NDV8mcLXNtUWc8ClRhN24SmEML3atUVA2vXvdY1DziSdUkyTHuT-6EJBKO-iU-5cKikWybIcar8UMTOuowO_revQ9ivvEh274_9CBpUHMPeFrZgG-TQf0JnD2ppWelKNi_M-Its1ySrYFXf2EIzD1rOHj03fXkCr6OnyfA5qGgSAozS_ioQWvd1rFSoE8FjxSOFqI3fkSeJJJ5znuNAaMpDSkx4Yzvb0DgtpAWGGGMs-Cm0ikVBZ8CQxxxTiflgYEFvSCqyePcmiFWpMXVhB3obyWVYYYhbKov3zMUSYZoZWWdW1lkl6w7c1Cs-PH7GH3PbVrb1vEqsHehuDierHliZRSbOEQ665_z3VRewa5nhfbakC63Vck2XsIOfq3m5vHJ35xuhzr6g
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NS8MwFH_MKagHv6Y4ndqDF8G6Nmma9qjTMXEODxN2K2n2KgPpZN0E_3vz0ZWBInjLIYHwkrzPvN8P4JJokLswEury6tSNx5grJE9dJkMMiRhHHC3ZBB8MotEofqnBddULg4jm8xne6KGp5Y-ncqFTZW0NVKI07RqssyAgvu3WqmoG2rJbvaueMImqoqQXt4e3HRUKEl9FqJQbXNIVI2RYVX6oYmNfurv_29ke7JR-pHNrD34fapgfwPYKumADoo79hu7ciZnmpSucSa7GX6j7Jp0SWPXNmWaOhS137i07fXEIr92HYafnlkQJriSxP3d5lvkZE8LLQk6ZoERImSnPIw3DCGlKaSoDnmHqYagCHN3bJpXbghmXnmSScXoE9Xya4zE4kjIq40imQaBhbzASqBHvVRgrYmXsvCa0l5JLZIkirsks3hMTTXhxomSdaFknpaybcFWt-LAIGn_MbWjZVvNKsTahtTycpHxiRUJUpMMNeM_J76suYLM3fO4n_cfB0ylsaZ54mztpQX0-W-AZbMjP-aSYnZt79A27DMHn
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Control+Barriers+in+Bayesian+Learning+of+System+Dynamics&rft.jtitle=IEEE+transactions+on+automatic+control&rft.au=Dhiman%2C+Vikas&rft.au=Khojasteh%2C+Mohammad+Javad&rft.au=Franceschetti%2C+Massimo&rft.au=Atanasov%2C+Nikolay&rft.date=2023-01-01&rft.pub=IEEE&rft.issn=0018-9286&rft.volume=68&rft.issue=1&rft.spage=214&rft.epage=229&rft_id=info:doi/10.1109%2FTAC.2021.3137059&rft.externalDocID=9658123
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0018-9286&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0018-9286&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0018-9286&client=summon